1. Using a Cellular Automata Urban Growth Model to Estimate the Completeness of an Aggregated Road Dataset Tiernan Erickson U.S. Census Bureau

2. Address Canvassing: Census workers compare what they see on the ground to what is shown on the Census Bureau's address list. Based on their findings, the census workers will verify, update, or delete addresses already on the list, and add addresses that are missing from the list. At the same time, they will also update maps so they accurately reflect what is on the ground. Housing unit addresses verified: 145 million Census workers hired for address canvassing: 140,000 Background Source: U.S. Census Bureau. Address Canvassing Facts/Statistics. Retrieved June 16, 2012, from http://2010.census.gov/ news/press-kits/one-year-out/address-canvasing/ address-canvassing-facts-statistics.html

3. Geographic Support System (GSS) Initiative: Integrated program in support of the 2020 Census: Improved address coverage Continual spatial feature updates Enhanced quality assessment and measurement A targeted, rather than full, address canvassing operation during 2019 in preparation for the 2020 Census. Collaboration with federal, state, local, and tribal governments and other key stakeholders to establish an acceptable address list for each geographic entity. Background Source: U.S. Census Bureau. Geographic Support System (GSS) Initiative. Retrieved June 16, 2012, from http://www.census.gov/geo/www/gss/index.html

4. Geographic Support System (GSS) Initiative: Background Positional Accuracy Thematic Accuracy Temporal Accuracy Logical Consistency Completeness?

5. Spatial Data Completeness

6. Detroit, MI Source: Google Maps Spatial Data Completeness

7. South of Austin, TX Source: Google Maps Spatial Data Completeness

8. 8 Source: Project Gigalopolis http://www.ncgia.ucsb.edu/projects/gig/v2/About/abImages/apps/wash-balt_1792-2100.htm Urban Growth Forecasting Models

9. 9 Image Source: Cutsinger (2006) Urban Growth Forecasting Models Cellular Automata Urban Growth Models Generate realistic urban patterns Integrate the modeling of the spatial and temporal dimensions of urban processes. -Santé, et al. (2010)

10. 10 Urban Growth Forecasting Models

11. 11 SLEUTH Model S - Slope L - Landuse E - Exclusion U - Urban Extent T - Transportation H - Hillshade

12. 12 SLEUTH Model S - Slope L - Landuse E - Exclusion U - Urban Extent T - Transportation H - Hillshade

13. 13 SLEUTH Model S - Slope L - Landuse E - Exclusion U - Urban Extent T - Transportation H - Hillshade

14. 14 SLEUTH Model S - Slope L - Landuse E - Exclusion U - Urban Extent T - Transportation H - Hillshade

15. 15 SLEUTH Model S - Slope L - Landuse E - Exclusion U - Urban Extent T - Transportation H - Hillshade

16. 16 SLEUTH Model S - Slope L - Landuse E - Exclusion U - Urban Extent T - Transportation H - Hillshade

17. Model Parameters (“Urban DNA”): Diffusion Breed Spread Slope Resistance Road Gravity SLEUTH Model

18. Source: Clarke et al. (1997)

19. SLEUTH Model Source: Clarke et al. (1997)

20. 20 Source: Project Gigalopolis http://www.ncgia.ucsb.edu/projects/gig/v2/About/abImages/apps/wash-balt_1792-2100.htm SLEUTH Model

21. SLEUTH-3r More efficient Jantz et al. (2009) Used to model entire Chesapeake Bay drainage Uses different measures of “fit” to compare prediction with actual for calibration and validation SLEUTH and SLEUTH- 3r are free and run on Unix (Cygwin) Requires 1.5G RAM

22. Use SLEUTH-3r NLCD available for 1992, 2001, and 2006. Calibration: 1992 – 2001 Prediction: 2006(est.) Validation: 2006(est.) vs. 2006 (actual) Methods

23. 23 Methods

24. 24 Expected Project Output SLEUTH's Output: Series of rasters showing percent likelihood of new development for each cell, for each year between 2001 and 2006 Research Product: Aggregate prediction values to the tract level. For each tract last updated more than a year previous to 2006, the percent-likelihood of development will be summed for each year since last updated. If the sum total of unaccounted-for growth is above a threshold, then the tract is in need of updating.

25. 25 Significance Estimate of completeness of aggregated road dataset (TIGER) Incomplete in areas where: 1) Road growth is occurring rapidly, and 2) Have not been updated recently Complete (save resources) in areas where: 1) Little or no growth, or 2) Have been updated recently

26. 26 Limitations SLEUTH does not get into the causes of urban growth (as inputs). Instead, focuses on measuring and predicting a region's growth pattern ("Urban DNA") regardless of underlying causes. Diffusion Breed Spread SLEUTH does not put constraints on growth, such as: Population Growth Projections Economic Growth Projections Extrapolates from previous growth. Uses Self-Modification Rules ("Boom" and "Bust") to produce realistic S-shaped growth curve projections based on recent growth, but not constrained to match other models' projections. Slope Resistance Road Gravity

27. 27 Limitations Are SLEUTH's predictions of urban growth a satisfactory proxy for predictions of road network growth? This study will provide an answer to that question Compare actual 2006 road network growth using same goodness-of-fit metrics that SLEUTH uses for Validation.

28. 28 Possibilities Adapt model to constrain the outputs to match population or economic growth projections Adapt the model to make use of demographic inputs New NLCD data (2011) scheduled for release in December 2013 Updated projections for the rest of this decade Imagery for specific areas could be processed to create more frequent land cover datasets with which to update predictions.

29. 29 Possibilities Eventually it could be useful to model urban growth for the entire country. SLEUTH's creator, Keith C. Clarke, has said that he would like to see the model used for the entire United States (Clarke, 2008 and 2011). Jantz et al. 2009 study of the entire Chesapeake Bay watershed (208 counties) remains the largest application of SLEUTH to date that I found in the literature. An eventual nation-wide simulation could provide estimates of completeness of coverage for TIGER that could support the Census Bureau's stated goals for targeted update operations.

30. 30 Next Steps Download and install Cygwin (Unix environment for Windows) Read Cygwin documentation Download and install SLEUTH software Read Cygwin documentation Download remaining TIGER datasets (Tract, All LInes) Convert TIGER/Line files to shapefiles Merge DEMs for test county Run SLEUTH on test county, probably my hometown for familiarity: Pima County, AZ (04019) Estimate reasonable number of counties to process Select counties for study Streamline data download, setup, model run process Download data for additional counties Run SLEUTH model on counties Validate output to 2006 land cover Validate 2006 roads to 2006 land cover Simulate TIGER update dates by Tract Compare update dates to urban growth predictions by Tract Create percent-likelihood-incomplete estimates by tract

31. 31 Questions? Comments?